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Modeling of Intensity Priors for Knowledge-Based Level Set Algorithm in Calvarial Tumors Segmentation

  • Aleksandra Popovic
  • Ting Wu
  • Martin Engelhardt
  • Klaus Radermacher
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)

Abstract

In this paper, an automatic knowledge-based framework for level set segmentation of 3D calvarial tumors from Computed Tomography images is presented. Calvarial tumors can be located in both soft and bone tissue, occupying wide range of image intensities, making automatic segmentation and computational modeling a challenging task. The objective of this study is to analyze and validate different approaches in intensity priors modeling with an attention to multiclass problems. One, two, and three class Gaussian mixture models and a discrete model are evaluated considering probability density modeling accuracy and segmentation outcome. Segmentation results were validated in comparison to manually segmented golden standards, using analysis in ROC (Receiver Operating Curve) space and Dice similarity coefficient.

Keywords

Gaussian Mixture Model Segmentation Accuracy Medical Image Computing Multiclass Problem Prior Probability Density Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Aleksandra Popovic
    • 1
  • Ting Wu
    • 1
  • Martin Engelhardt
    • 2
  • Klaus Radermacher
    • 1
  1. 1.Chair of Medical Engineering, Helmholtz-Institute for Biomedical EngineeringRWTH Aachen UniversityGermany
  2. 2.Neurosurgical ClinicRuhr-University BochumGermany

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